From Education

Visioneer Design Challenge is a statewide learning program and competition for high school and middle school students interested in design arts connecting with professional designers in each field. 3 Years ago, Ryan, Amanda, Arnie and I volunteered to run a challenge that introduced game design principles. The challenge consists of two activities, a long term challenge to create a game using gamemaker, and a short-term one day game jam. The attendance for the first year was average, I think we had about 15 students or so. Flash-forward to today where we had 57 kids sign up. Yeah… I think it’s gotten a bit popular.

The day long challenge is intense. We have about 6 hours to create, playtest, and judge games. Every year we wonder if we’ll run out of time, but every year we end up with a collection of new games made by students that have never made games before.

Game Description:
In MACBETH, players, called “analysts,” are presented with a fictional scenario of an impending terrorist attack, and their task is to figure out who the suspect is, where the attack will occur, and what method of attack will be used. MACBETH is a turn-based game, where a human participant plays cooperatively with two non-playable characters (NPCs). In any one turn, analysts are able to gather two pieces of information about the suspect, location, and/or weapon from a combination of intel sources. After gathering information, the human player can generate a hypothesis or aid another analyst (an NPC) if they have information proving or disproving the other analyst’s current hypothesis. Throughout the game, analysts learn about the cognitive biases, and receive implicit and/or explicit feedback (based on condition) encouraging them to delay making a hypothesis, seek disconfirming information that can be used to disprove their hypothesis, and offer alternative hypotheses in their efforts to mitigate confirmation bias.

Role:
Lead Game Designer / Game Prototype Designer.

Awards:

2014 Best Business Game – Intelligence Crisis: Codename MACBETH Serious Games Showcase.

Game Description:
Fair Play was created as a way to address issues of implicit bias in a safe environment. In Fair Play, the player assumes the role of Jamal, a new graduate student. Along the way, the player is face with bias scenarios, which they must address in order to successfully make it through their first year. It was our hope that, through gameplay, players will be exposed to various anti-bias strategies, which they will incorporate into their daily lives.

Today we made sure that everyone had python installed and went over some basics of the command line terminal. Essentially, we opened our terminal, typed “python” and messed around with the interpreter. We then created our first script, saved as “test.py” with the line:

print “hello world”

and successfully ran it on our terminal.

We then messed a bit with variables and a for loop.

The scripts we ran are attached. If you weren’t here, try and get them to run on your machine.

Game Description:
Alpha Racers is a fun and exciting new learning game that will help your child learn their letters and sounds! Unlike many alphabet games currently available, Alpha Racers is actually a real racing game, where your child learns their letters as they race for the trophy!

Quick update today. I came across a cool tool recently. In an effort to graduate, I’ve been trying to write more. Thanks to research, I’ve also been interested in what text analysis tools can determine based on your input. That’s how I came across this. I write Like is a cool website that lets you upload a piece of writing and then performs some analysis to determine what famous author you write like. I apparently write like Agatha Christie. I would rather write like Douglas Adams, but this is cool too.

Like I write like wordle lets you upload a doc and then creates a word cloud based on word frequency. For example, here’s mine.

Cohmetrix is a little more academic. In their words “Coh-Metrix calculates the coherence of texts on a wide range of measures. It replaces common readability formulas by applying the latest in computational linguistics and linking this to the latest research in psycholinguistics.” In other words, it takes your writing and calculates cool stats like grade level.

This post was a long time coming. About a half a year ago, a group of us decided to create the worst game ever. It (the game) was terrible, so I guess we succeeded.

During the emerging scholars meeting, a couple of people talked about using some of the time to create a tangible product. The first idea was a game jam, but people didn’t seem particularly thrilled to try and create a game (from to start to finish) in about 2 hours. Making a good game from scratch in such a short amount of time was definitely a lofty goal, so instead we created the first Worst Game Ever Jam.

Knowing that we were making the worst game ever worked well for two reasons. First, because we weren’t worried about making a “good game” we were able to create something quickly without worrying about quality. Second, because it our goal was to make a terrible game, we had plenty of ideas.

Brainstorming:

We used the KJ technique to brainstorm what makes a terrible game. By the way, I highly recommend the KJ technique to anyone trying reach a consensus quickly. It’s worked very well in several occasions. Here are some topics that came up:

After wading through the suggestions, we decided to make an educational game using LOL cats, micro transactions, and incoherent instructions.

First Steps:

We decided to give our project a title. I forget what the original title was, but after we put it through Bable Fish a couple of times we ended up with “You can play a role in reshaping the beautiful”. Pure gibberish. It was perfect.

We then split up the work load in a way that everyone could contribute. We needed art, narrative, design, music, and coding. I can’t stress how much dropbox helped to coordinate this. Once everyone had a way to contribute, we set out to create an abomination.

The Aftermath:

After 2 hours of talking about what makes a terrible game, we ended up with this. Play it at your own risk. We’re currently trying to sell it to Zynga.

(The ‘kickin’ sound track, by Osvaldo Jimenez, is set to drop NEVER. Maybe we’ll put it up on soundcloud.)

Reflection:

As Jim Gee, a co-author of the game, pointed out, thinking about what makes a terrible game was actually a great way to avoid those pitfalls in the future. I think this is terribly important for game designers (and people who work with games, but havent tried to create one themselves). Bottom line, making a terrible game forces you to think about why a game is terrible and lets you work towards fixing those problems. It’s also a good way to get out those frustrations. I plan on doing another Worst Game Ever jam in the future.

Have an opinion about what makes a terrible game? Leave it in the comments.

Every so often I get a request to help with some techy stuff. To be completely honest, I love doing this kinda stuff because I like to make things. I also like to see things other people create things. Helping people to get blogs up seems to fill both of these needs. So, without further delay, how to build a website form scratch for 20$.

First, find a domain that you like:

A domain is a web address that people type into their browser to get to your site. These generally cost about 10$ and can be registered for about a year before they need to be renewed. I like Dreamhost (http://dreamhost.com/domains/) and have my domains there. You can do the same, or use any other domain service to buy your domain. Be creative and try to think of an address that people will remember. Sometimes, the name you want won’t be available so try a lot of different variations.

Hosting:

Now that you have a Domain, it’s time to decide where you would like to host your content. In other words, you’ll be renting a computer from a server to hold the content that you put up (in this case, your blog). You can use your own computer to host a website, but that’s a little more complicated, and I won’t go into it for this post. Again, for hosting I use Dreamhost which clocks in at 9$/month. Dreamhost provides unlimited bandwidth, allows you to host multiple websites for free, and maintains the computers for you (which I think is helpful if you can’t maintain a server yourself).

Once you’ve done both of the above, using your same account, you can now install your blog. For this step we’ll use wordpress (http://wordpress.org/) and Dreamhost’s installer.

Log into your dreamhost panel and click on the domain link on the side.

This should show your domain(s) and whether or not they are being hosted. Click on “add hosting” under the actions column and it should take you to another screen that looks like this:

Lots of information here, but don’t worry about that for now. Right now, just click on “Fully host domain”. The quick version of what you’re doing here is: you’re telling dreamhost to make a folder on their computer so that when people go to your domain they’ll see the stuff in that folder. In the future I might go into putting stuff into these folders using FTP, but for now we’ll just install wordpress.

Installing wordpress:

Almost done. Next, click on “goodies” and then “one-click installs” while logged into your dreamhost account. This will take you to a page that lists a lot of services you can install on your site (this includes wordpress).

Click on the wordpress link and it’ll take you to the install page.

Click on custom installation and then select your domain from the dropdown menu. Dreamhost should now send you an email saying it’s installing wordpress and will notify you when your site is ready. Once you get the “Finished installing” email from dreamhost, follow the directions to set up an admin account. You can now post things onto your blog!

That’s it. You’re done. You can now go to the web address you bought and start sharing your thoughts. If you have any questions, or want to show off your new site, leave a comment.

This weekend I attended the MacArthur emerging scholars meeting in Arizona. The following post contains some reflections from that meeting.

Day1

The most interesting thing that came out of our conversation, for me anyway, where the issues surrounding designing for all people. Don’t get me wrong, I think it’s great to be inclusive, but I see designing for everyone at all times as an insurmountable feature creep. I think we would be more successful in education reform if we limit our scope and do something right before we start to think about the ways it doesn’t work. At least then, once we finish, we can reiterate and add additional features. If we change it and add features before we even make the thing, while it’s still conceptual, there’s no way we can build it… If we can’t even build it, then there’s not way we’ll have reform.

(this was originally published in NOTALAND in 2009, because the site is no longer operational I have reposted it here.)

Thorndike, Edward, (1911) Animal Intelligence, New York, The MacMillan Company

Thorndike presents his findings about intelligence with the results from his studies involving cats, dogs and chicks. Thorndike studies imitation, discovery and association using different methods for each. The first, and perhaps the most striking, of Thorndike’s included the use of a specialized box. The subject (either a cat or a dog) is placed into a box that requires the animal to execute a specific action in order to be rewarded with food. This action ranged from pushing buttons and levers, or biting and rubbing against strings. While the animals are able to accomplish the various tests Thorndike does not attribute this to intelligence. Instead he simply states that these actions are due to associations. Thorndike’s other experiments include instructing animals on how to accomplish a task (by physically moving their paw), seeing if animals learned to imitate an act after observing others, and finally evaluating if animals make connections between specific commands and rewards without first seeing the reward. Thorndike reached the conclusion that animals do not posses higher forms of reasoning. Thorndike then assumes that an animal’s “consciousness” is similar to the sensation we feel when we swim. We can feel the water, but, unless we make an effort, we do not “think” about water and it’s properties.

Skinner gives us a brief history of the deconstruction of mystic processes to what he now titles Behaviorism. First he sites how historically there have been two entities, the mind and the body. This is followed by a reflection on how the mind is considered so complex that we feel complex methods must be used to assert how the mind works. Skinner goes on to illustrate how the works of Pavlov and Thorndike seem to indicate that the mind needs no special method of inquiry. Instead Skinner suggests that these earlier studies break every action down into a stimulus, some unseen process, and the resulting response. Skinner then argues that since the second part, the unseen process, does not provide us with any advantage, in terms of manipulating actions, it can be ignored. Thus Skinner postulates that a further study of stimulus and responses, with the aid of some motivator (a reinforcer), will yield great insights about the human mind.

The Artificially Intelligent Mouse

Following the theory of behaviorism we can now teach the artificial mouse to get to a certain spot on our board by rewarding it when it has reached the desired goal. Because we cannot give an artificial mouse a physical reward we must think of a similar way to motivate our mouse. In Thorndike’s famous puzzle box experiments Watson uses an animal’s hunger as motivation and provides food as a reward. We can model this by programming our mouse to seek out high numbers in much the same way that animals seek food. Keeping this same approach we can then reward the mouse with the number 100 once it has done what we wanted. In the same way that Animals narrowed down their actions, or as Thornlike said the associations get “stamped in”, our mouse will give a discount association to the spaces that are close, but not directly next, to the goal.

In our example we condition our mouse to reach the top left position of the board. The mouse will make random movements until it reaches the desired spot at which point we will give it a reward of 100. The mouse will be allowed as many trials as the users want and will continue to refine it’s path every iteration.

The AI Mouse example Illustrates how ideas from behaviorism are used in the field of artificial intelligence. The same way that animals are thought to adopt behaviors that benefit them directly, the artificial mouse is programmed to seek out the optimal square on a given board. Following the same analogy animals are thought to associate rewards with the actions that preceded it, the artificial mouse also associates a reward from a new square with the square that preceded it. After a few runs we can then see how the mouse converges to an optimal solution.

Criticism.

Criticism of reinforcement being used as a way to replicate intelligence include Skinner’s own argument for the use of his method. Skinner stated that because we could not see the inner workings of the mind we could not successfully evaluate the processes that take place there. Skinner then argues that because it would all be speculation, we should avoid these internal workings and focus on what we can evaluate. In much the same way, this example successfully produce the results given the proper reinforcement but may not give us any insight into true, human, intelligence. It could be argued that even a real mouse has motivations other than hunger that drive it to look for food and that because we ignore the inner workings of the mouse’s mind we are not accurately interpreting the scenario.

Another critique of this approach is the fact that if we fail to give a proper reinforcement our artificial mouse would not stay in the square we desire. In the same way some have argued that actions learned using reinforcement do not persist once the reinforcement is no longer present.

In Chomsky’s literary review of B.F. Skinner’s “Verbal Behavior” Chomsky analyzes Skinner’s claim that language is a system of stimulus/response pairs. Chomsky criticizes skinner’s definition of reinforcement as one that jumbles anything remotely related to acquisition and retention together. In the same way Chomsky argues that reinforcement is also ill defined in this context to that point, he argues, that they lose any objective meeting they might have ever had. Skinner’s definition of Stimulus is also considered too wide encompassing whoever is talking, the subject of the discussion, and background information. Chomsky then goes on to challenge the way that skinner measures the degree of responses. He gives the example of how the phrase “it’s beautiful” uttered in a low tone may carry just as much if not more weight than the same response said in a high pitch. Chomsky also suggests that Humans do things at random without any conceivable reinforcement. He then argues that due to this randomness the precise care and set up that reinforcement learning is suggested to need cannot be generated. Because of these ill-defined terms and seemingly unsound experiments, Chomsky concludes that it’s difficult not only to falsify skinners claims, but also to validate them.